# Specific Lipidomic Shifts in Chronic Lymphocytic Leukemia at Diagnosis

## TL;DR

This study finds unique fat patterns in the blood of newly diagnosed chronic lymphocytic leukemia patients, which could help detect the disease earlier and guide treatment.

## Contribution

The study identifies specific lipidomic shifts in treatment-naïve CLL patients and links them to disrupted metabolic pathways.

## Key findings

- CLL patients had higher levels of carnitines and specific phospholipids compared to healthy individuals.
- Lipidomic profiling revealed enriched pathways in glycerolipid and phosphatidylcholine metabolism in CLL patients.
- Machine learning identified carnitines and ether-linked phospholipids as key discriminators for CLL.

## Abstract

Chronic lymphocytic leukemia (CLL) is a common type of adult blood cancer in which cells survive longer than normal, partly due to changes in how they process fats and other molecules for energy. This study examined the blood plasma of newly diagnosed patients who had not yet received treatment to identify unique patterns in lipid molecules. We found that patients with CLL had higher levels of certain fats, including carnitines and specific phospholipids, compared with healthy individuals. By analyzing these lipid changes using predictive bioinformatics tools, we identified that several pathways involved in lipid metabolism are likely disrupted. These findings improve our understanding of how this disease alters the body’s metabolism and could inform future research on biomarkers for earlier disease detection and treatment development.

Background: Chronic lymphocytic leukemia (CLL) is the most common adult leukemia and is characterized by dysregulated apoptosis and metabolic reprogramming, including alterations in lipid metabolism. However, the plasma lipidome of newly diagnosed, treatment-naïve CLL patients remains insufficiently characterized. This study aimed to define disease-specific plasma lipidomic alterations, identify discriminatory lipid species, and investigate associated metabolic pathways. Methods: The study cohort consisted of 41 participants (median age 75 years, range: 40–86), including 30 newly diagnosed, treatment-naïve CLL patients (median age 75 years, range: 40–86) and 11 age- and sex-matched healthy controls (median age 75 years, range: 41–85). Targeted lipidomic profiling was performed on plasma samples using liquid chromatography with tandem mass spectrometry (LC-MS/MS). Data processing was conducted in R using LipidSigR. Statistical analyses employed the Wilcoxon–Mann–Whitney test with Benjamini–Hochberg correction. To address data dimensionality, Boruta machine learning and pathway enrichment analyses were applied. Gene–lipid associations were further explored using GATOm, followed by Metascape analysis to identify enriched biological processes. Results: A total of 124 lipid species from five major classes (phosphatidylcholines, lysophosphatidylcholines, sphingomyelins, ether-linked phosphatidylcholines, and acylcarnitines) were quantified. CLL patients exhibited significant enrichment of acylcarnitines, saturated phosphatidylcholines, and sphingolipids compared with controls. Principal component analysis showed partial separation by disease status. Machine learning identified carnitines and ether-linked phospholipids as key discriminators. Integrated gene–lipid analyses revealed significant enrichment of lipid metabolism-related pathways, particularly glycerolipid and phosphatidylcholine metabolism, as well as lipid catabolism, ether lipid metabolism, and fatty acid metabolism. Conclusions: Treatment-naïve CLL patients display distinct plasma lipidomic signatures indicative of disease-specific metabolic reprogramming. Integrated lipidomic and predictive pathway analyses suggest disruptions in lipid metabolic pathways and highlight carnitines and ether-linked phospholipids as biological markers warranting further investigation as potential CLL biomarkers.